Publications by Torsten Butz | EPFL Graph Search

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

We propose a deep study on tissue modelization and classification techniques on T1-weighted MR images. Six approaches have been taken into account to perform this validation study. We consider first the Finite Gaussian Mixture model (A-FGMM) and a Bayes classification. Second method is the same as A-FGMM but introducing a Hidden Markov Random Field (HMRF) model to encode spatial information and classification is then performed by Maximum a Posteriori (MAP). Third, we study a method that models mixture tissu

Neuronavigation systems are usually subject to inaccuracy due to intraoperative changes like brain shift or tumor resection. In order to correct for these deformations a biomechanical model of the brain is proposed. Not only elastic tissues, but also fluids are modeled, since an important volume of the head contains cerebrospinal fluid, which does not behave like soft tissues. Unlike other approaches, we propose to solve the differential equations of the model by means of the boundary element method, which

The signal processing community is increasingly interested in using information theoretic concepts to build signal processing algorithms for a variety of applications. A general theory on how to apply the mathematical concepts of information theory to the ...

We propose an information theoretic model that unifies a wide range of existing information theoretic signal processing algorithms in a compact mathematical framework. It is mainly based on stochastic processes, Markov chains and error probabilities. The p ...

We propose a deep study on tissue modelization and classification Techniques on T1-weighted MR images. Three approaches have been taken into account to perform this validation study. Two of them are based on Finite Gaussian Mixture (FGM) model. The first one consists only in pure gaussian distributions (FGM-EM). The second one uses a different model for partial volume (PV) (FGM-GA). The third one is based on a Hidden Markov Random Field (HMRF) model. All methods have been tested on a Digital Brain Phantom i

This paper presents a validation study on statistical non-supervised brain tissue classification techniques in MR images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of cla ...

We present a method that exploits an information theoretic framework to extract optimal audio features with respect to the video features. A simple measure of mutual information between the resulting audio features and the video ones allows to detect the active speaker among different candidates. The results show that our method is able to exploit the shared speech information contained in audio and video signals to recover their common source.

Intra-operative MR imaging is an emerging tool for image guided (neuro)surgery. Due to the small size of the magnets and the short acquisition time, the images produced by such devices are often subject to distortions. In this work, we show the particular case of images provided by an ODIN device (Odin Medical Technologies, Newton, MA 02458, USA). Such images suffer from geometric distortions and an important bias field in the luminance. In order to simultaneously correct these deformations, we propose to r

This document presents a brief description of the Medical Image Processing (MIP) library developed by the Brain Group of the Signal Processing Institute (ITS) at Swiss Federal Institute of Technology (EPFL). This library is a result of more than three years of research by Meritxell Bach Cuadra, Torsten Butz, Eduardo Solanas and Olivier Cuisenare, carried out under the supervision of Jean-Philippe Thiran. You can find more detailed information about the library at our web page located at http://ltswww.epfl.c

Torsten Butz

Chat with Graph Search

Validation of Tissue Modelization and Classification Techniques in T1-weighted MR Brain Images

Brain Shift Correction Based on a Boundary Element Biomechanical Model with Different Material Properties

From error probability to information theoretic signal and image processing

From Error Probability to Information Theoretic (Multi-Modal) Signal Processing

Validation of Tissue Modelization and Classification Techniques in T1-Weighted MR Brain Images

Comparison and Validation of Tissue Modelization and Statistical Classification Methods in T1-weighted MR Brain Images

A multimodal approach to extract optimized audio features for speaker detection

Simultaneous Registration and Bias Correction of Brain Intra-operative MR Images

MIP: The ITS-EPFL Medical Image Processing Library

Correction to "Comparison and Validation of Tissue Modelization and Statistical Classification Methods in T1-Weighted MR Brain Images"

A new brain segmentation framework

Multi-Modal Medical Image Registration: from Information Theory to Optimization Objective

Fast non-rigid resistration and model-based segmentation of 3D images using mutual information

Feature-space mutual information for multi-modal signal processing, with application to medical image registration

Shot Boundary Detection with Mutual Information

Feature Space Mutual Information in Video-Speech Sequences

Affine Registration with Feature Space Mutual Information Medical Image Computing and Computer-Assisted Intervention

Pre- and Intra-operative Planning and Simulation of Percutaneous Tumor Ablation

Supervised nonparametric information theoretic classification

Intaoperative Segmentation and Nonrigid Registration for Image Guided Therapy